PSICA: a fast and accurate web service for protein model quality analysis.

Wenbo Wang,Yi Shang,Dong Xu,Junlin Wang,Zhaoyu Li

doi:10.1093/nar/gkz402

Abstract

This paper presents a new fast and accurate web service for protein model quality analysis, called PSICA (Protein Structural Information Conformity Analysis). It is designed to evaluate how much a tertiary model of a given protein primary sequence conforms to the known protein structures of similar protein sequences, and to evaluate the quality of predicted protein models. PSICA implements the MUfoldQA_S method, an efficient state-of-the-art protein model quality assessment (QA) method. In CASP12, MUfoldQA_S ranked No. 1 in the protein model QA select-20 category in terms of the difference between the predicted and true GDT-TS value of each model. For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model. Additionally, PSICA implements MUfoldQA_C, a new consensus method based on MUfoldQA_S. In CASP12, MUfoldQA_C ranked No. 1 in top 1 model GDT-TS loss on the select-20 QA category and No. 2 in the average difference between the predicted and true GDT-TS value of each model for both select-20 and best-150 QA categories. The PSICA server is freely available at http://qas.wangwb.com/∼wwr34/mufoldqa/index.html.

Highlights

The three-dimensional (3D) structure of a protein is essential in studying its functions [1]
For a given predicted 3D model, PSICA generates (i) predicted global GDT-TS value; (ii) interactive comparison between the model and other related known protein structures; (iii) visualization of the predicted local quality of the model; and (iv) JSmol rendering of the model
For the select-20 quality assessment (QA) category, which is similar to practical protein structure prediction situation, where a small number of predictions are generated and their qualities vary greatly, MUfoldQA S and MUfoldQA C performed significantly better than other methods in terms of average GDTTS differences, ranked No 1 and No 2, respectively, outperforming the third place by 35.8% and 32.0%

Summary

Introduction

The three-dimensional (3D) structure of a protein is essential in studying its functions [1]. Computational 3D protein structure prediction is important since experimental methods including X-ray crystallography, electron microscopes and nuclear magnetic resonance (NMR) are all costly and time consuming [2]. Predicting 3D structures using computational methods can be much faster and cheaper. It is vital to find a reliable method to evaluate the quality of predicted models [3]. Over the past 20 years, many protein model quality assessment (QA) methods have been proposed [4,5]. The size and quality of the model pool used by multi-model methods have great impact on their QA results [15]. Single-model QA methods use potential functions and/or machine learning. Machine learning has been used to aggregate various potential functions to achieve improved results

Methods

Results

Conclusion